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Determining the Evolution of an Alpine Glacier Drainage System by Solving Inverse Problems

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Determining the Evolution of an Alpine Glacier Drainage System by Solving Inverse Problems Journal of Glaciology Determining the evolution of an alpine glacier drainage system by solving inverse problems Inigo Irarrazaval1 , Mauro A. Werder2,3 , Matthias Huss2,3,4 , 1 1 Article Frederic Herman and Gregoire Mariethoz 1 Cite this article: Irarrazaval I, Werder MA, Huss Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, M, Herman F, Mariethoz G (2021). Determining Switzerland; 2Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland; 3Swiss the evolution of an alpine glacier drainage Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland and 4Department of system by solving inverse problems. Journal of Geosciences, University of Fribourg, Fribourg, Switzerland Glaciology 67(263), 421–434. https://doi.org/ 10.1017/jog.2020.116 Abstract Received: 12 June 2020 Our understanding of the subglacial drainage system has improved markedly over the last decades Revised: 16 December 2020 Accepted: 17 December 2020 due to field observations and numerical modelling. However, integrating data into increasingly First published online: 22 January 2021 complex numerical models remain challenging. Here we infer two-dimensional subglacial chan- nel networks and hydraulic parameters for Gorner Glacier, Switzerland, based on available field Key words: data at five specific times (snapshots) across the melt season of 2005. The field dataset is one of Glacier modelling; glaciological instruments and methods; subglacial processes the most complete available, including borehole water pressure, tracer experiments and meteoro- logical variables. Yet, these observations are still too sparse to fully characterize the drainage sys- Author for correspondence: tem and thus, a unique solution is neither expected nor desirable. We use a geostatistical Inigo Irarrazaval, generator and a steady-state water flow model to produce a set of subglacial channel networks E-mail: [email protected] that are consistent with measured water pressure and tracer-transit times. Field data are used to infer hydraulic and morphological parameters of the channels under the assumption that the location of channels persists during the melt season. Results indicate that it is possible to identify locations where subglacial channels are more likely. In addition, we show that different network structures can equally satisfy the field data, which support the use of a stochastic approach to infer unobserved subglacial features. 1. Introduction Water flow at the ice/bedrock interface exerts controls on ice flow (e.g., Iken, 1981), glacier erosion (e.g., Herman and others, 2011), sediment transport (e.g., Beaud and others, 2018), as well as catchment hydrology (e.g., Verbunt and others, 2003). Hence, a significant scientific effort has been dedicated to characterizing the spatial distribution of subglacial systems and their temporal evolution by both field data acquisition and numerical modelling. Pioneer work conceptualized the subglacial drainage system with two main components: a network of subglacial channels with fast water flow (Röthlisberger, 1972;Nye,1976) and a distributed system with the slow flow (Lliboutry, 1968; Weertman, 1972). In addition, it has been observed that areas of a subglacial drainage network can become temporally or permanently hydraulic- ally disconnected, which can be described as the third component of subglacial systems (e.g., Iken and Truffer, 1997; Hoffman and others, 2016; Rada and Schoof, 2018). The study of subglacial systems has focused on both field observations and numerical mod- elling. Field techniques that have been employed include water level measurements in bore- holes drilled into the glacier (e.g., Hubbard and others, 1995; Rada and Schoof, 2018), dye-tracer tests in moulins (e.g., Nienow and others, 1998; Schuler and others, 2004; Werder and others, 2009; Chandler and others, 2013) and glacial speleology expeditions to survey portions of channel networks (e.g., Gulley and others, 2014). More recently, seismic and other geophysical methods have also provided insights into the evolution and structure of the subglacial systems (e.g., Gimbert and others, 2016; Church and others, 2019; Zhan, 2019; Lindner and others, 2020; Nanni and others, 2020). In addition to field methods, a range of subglacial numerical models have been built, which we divide here into two categories: (1) subglacial routing models and (2) subglacial evolution © The Author(s), 2021. Published by models. Routing models need to make an assumption for the hydraulic potential to compute Cambridge University Press.. This is an Open Access article, distributed under the terms of the water flow direction. Most commonly, the water pressure is assumed equal to the ice over- the Creative Commons Attribution- burden pressure to which the elevation potential is added to get the hydraulic potential, as was NonCommercial-ShareAlike licence (http:// first proposed by Shreve (1972). Then, a routing algorithm (e.g., O’Callaghan and Mark, 1984; creativecommons.org/licenses/by-nc-sa/4.0/), Tarboton, 1997) calculates a map of the upstream area or accumulated flow, where locations which permits non-commercial re-use, with higher upstream areas are conceptualized as channels. Routing models have been widely distribution, and reproduction in any medium, provided the same Creative Commons licence used to study subglacial systems, with applications including channel network topology deter- is included and the original work is properly mination (e.g., Richards and others, 1996), the occurrence of subglacial lakes (e.g., Livingstone cited. The written permission of Cambridge and others, 2013) and delineation of subglacial basins (e.g., Chu and others, 2016). University Press must be obtained for Furthermore, Banwell and others (2013) and Arnold and others (1998), constructed a channel commercial re-use. network structure based on routing methods on which they then calculated the water pressure and channel radii evolution. One of the limitations of such routing models is that only one single subglacial channel network is obtained and then further used for all numerical experi- cambridge.org/jog ments. Moreover, when combining the surface elevation and bedrock elevation, different Downloaded from https://www.cambridge.org/core. 30 Sep 2021 at 23:07:20, subject to the Cambridge Core terms of use. 422 Inigo Irarrazaval and others sources and spatial scales must be either downscaled or upscaled water pressure from boreholes is used to condition the subglacial to reach a common resolution, which in turn adds uncertainties. system through an inversion procedure. In addition, tracer-transit Efforts to explore the variability of the subglacial network struc- times from dye-tracer tests carried out from moulins are used to tures have considered the effects of varying a spatially uniform constrain the water velocities in the subglacial channels to a phys- flotation factor in Shreve’s equation. They have found that chan- ically reasonable range. nel topology and subglacial basin delineation are sensitive to the The remainder of this paper is organized as follows. Section 2 choice of a flotation factor (e.g., Chu and others, 2016). A first describes the model framework, which has its roots in Irarrazaval effort to consider a spatially two-dimensional (2-D) nonuniform and others (2019), however, incorporating new adaptations to fit term which adds variability to the channel network was presented real-world applications. Section 3 describes the field site, datasets, in Irarrazaval and others (2019). However, it was tested in a syn- modelling choices and assumptions to apply the framework to the thetic setup and used to infer the channel structure and hydraulic Gorner Glacier. Section 4 shows a set of subglacial systems condi- properties rather than the actual channel location. tioned to data across the melt season and parameter uncertainties The second category of models represents, besides water flow, provided by the inversion. The last section discusses the contribu- the process of the evolution of the subglacial drainage system. tion and limitations of a physical-geostatistical subglacial model First, 1-D models represent the evolution of one subglacial chan- and trade-offs in integrating data in complex numerical models. nel, for example, for describing glacial lake outburst floods (e.g., Flowers and others, 2004). More recently, 2-D subglacial evolu- tion models became capable of the spontaneous formation of 2. Methods channel network structures as the outcome of simulating subgla- The methodology consists of four steps. First, we use a geostatis- cial drainage system equations. They successfully incorporated tical subglacial channel generator to produce a set of uncondi- many of the known physical processes, including channel opening tioned subglacial systems states for a reference snapshot. The (by heat dissipation) and closing (by viscous-ice creep) allowing subglacial channel generator approximates the hydraulic potential the formation and evolution of channels and distributed systems by Shreve’s equation solely to produce channel networks based on (e.g., Schoof, 2010; Hewitt, 2011; Werder and others, 2013; Rada a flow routing approach (see Section 2.1). Second, we compute the and Schoof, 2018). However, in real-world applications, such hydraulic potential and water discharge inside the unconditioned approaches are difficult to be implemented and often
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